Thin-Slice Prostate MRI Enabled by Deep Learning Image Reconstruction.
Cancers (Basel)
; 15(3)2023 Jan 18.
Article
em En
| MEDLINE
| ID: mdl-36765539
ABSTRACT
OBJECTIVES:
Thin-slice prostate MRI might be beneficial for prostate cancer diagnostics. However, prolongation of acquisition time is a major drawback of thin-slice imaging. Therefore, the purpose of this study was to investigate the impact of a thin-slice deep learning accelerated T2-weighted (w) TSE imaging sequence (T2DLR) of the prostate as compared to conventional T2w TSE imaging (T2S). MATERIALS ANDMETHODS:
Thirty patients were included in this prospective study at one university center after obtaining written informed consent. T2S (3 mm slice thickness) was acquired first in three orthogonal planes followed by thin-slice T2DLR (2 mm slice thickness) in axial plane. Acquisition time of axial conventional T2S was 412 min compared to 437 min for T2DLR. Imaging datasets were evaluated by two radiologists using a Likert-scale ranging from 1-4, with 4 being the best regarding the following parameters sharpness, lesion detectability, artifacts, overall image quality, and diagnostic confidence. Furthermore, preference of T2S versus T2DLR was evaluated.RESULTS:
The mean patient age was 68 ± 8 years. Sharpness of images and lesion detectability were rated better in T2DLR with a median of 4 versus a median of 3 in T2S (p < 0.001 for both readers). Image noise was evaluated to be significantly worse in T2DLR as compared to T2S (p < 0.001 and p = 0.021, respectively). Overall image quality was also evaluated to be superior in T2DLR versus T2S with a median of 4 versus 3 (p < 0.001 for both readers). Both readers chose T2DLR in 29 cases as their preference.CONCLUSIONS:
Thin-slice T2DLR of the prostate provides a significant improvement of image quality without significant prolongation of acquisition time.
Texto completo:
1
Coleções:
01-internacional
Temas:
Geral
Base de dados:
MEDLINE
Tipo de estudo:
Observational_studies
Idioma:
En
Revista:
Cancers (Basel)
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Alemanha